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Power-Accuracy Tradeoffs in Human Activity Transition Detection

Power-Accuracy Tradeoffs in Human Activity Transition Detection. Prepared for DATE 2010 Dresden, Germany Jeffrey Boyd , Hari Sundaram, Aviral Shrivastava Arizona State University. The Ideal. Small Lightweight Unobtrusive Battery Life: Days, Weeks. On Low-power HW & SW:.

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Power-Accuracy Tradeoffs in Human Activity Transition Detection

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  1. Power-Accuracy Tradeoffs in Human Activity Transition Detection Prepared for DATE 2010 Dresden, Germany Jeffrey Boyd, Hari Sundaram, Aviral Shrivastava Arizona State University

  2. The Ideal • Small • Lightweight • Unobtrusive • Battery Life: Days, Weeks

  3. On Low-power HW & SW: “…hardware technology has a first-order impact on the power efficiency of the system, but you've also got to have software at the top that avoids waste wherever it can. You need to avoid, for instance, anything that resembles a polling loop because that's just burning power to do nothing.” (my emphasis) -Prof. Steve Furber “A Conversation with Steve Furber,” ACM Queue, Vol. 8 No. 2, February 2010.

  4. Tour Highlights • Why activity transition detection • Design Space • The great compromise • Design Space revisited • Low-power transition detection • Future tours

  5. Context & Motivation • Monitor patients at home • Stroke rehab – Is the patient using their impaired arm? • Replace surveys with objective data • Classify only when you need to—at the transitions • Do the minimum amount of work • “Do Nothing Well” WORK

  6. Window Size (Sw) Frame Size (Sf) Possible Transition Samples, Frames, Windows, and Panes Sampling Frequency (Fs) Window Pane

  7. Features & Temporal Resolution Fs={100, 50, 20, 10} Hz Sf={10, 20} samples per frame Sw={6, 8, 10, 12, 14, 16, 18, 20} seconds All combinations of accelerometer axis 4480 combinations!

  8. Experimental Setup y-axis • Five activities: Sitting, Standing, Walking, Eating, Reaching • Four combinations of activities • Wrist-mounted • Bluetooth Connectivity • 3-axis Accelerometer • Processing done offline in Matlab x-axis z-axis

  9. Sample Dataset & Evaluation • Sit – Eat - Walk • Peaks indicate times where the probability of transition is greatest • Detect peaks, then measure: • Precision: P=Hits/(Hits + False Positives) • Recall: R=Hits/(Hits + Misses) • F-Score: F=2*P*R/(P + R) • Reverse F-Score: RF = 1-F • Time for each combination to process test files

  10. Design Space & Pareto Optimal Points More Accurate Faster

  11. Sacrifice Little, Gain Much 5% Loss 5.5x Gain

  12. Optimal Points in Detail

  13. Scalars and Vectors

  14. 5% Loss 5.5x Gain Summary • Single-axis, simple feature • Vectors are (computationally) expensive • The Great Compromise • 5% better accuracy or 5x battery performance • Do Nothing Well

  15. Future Tour Offerings • Collect More Data! • Multiple users • Different Activities • Train activity classifiers • Build custom low-power device • Implement algorithm in device firmware • Reduce power by approximating features and classifiers • Directed Search (for best feature and time combinations) • Compare it with genetic algorithm and Monte Carlo search techniques

  16. Fragen - Questions Contact Info: Jeffrey Boyd Jeffrey.Boyd@asu.edu Hari Sundaram Hari.Sundaram@asu.edu Aviral Shrivastava Aviral.Shrivastava@asu.edu ?

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